Thumbnail generation for video

ABSTRACT

According to one implementation, a video processing system for performing thumbnail generation includes a computing platform having a hardware processor and a system memory storing a thumbnail generator software code. The hardware processor executes the thumbnail generator software code to receive a video file, and identify a plurality of shots in the video file, each of the plurality of shots including a plurality of frames of the video file. For each of the plurality of shots, the hardware processor further executes the thumbnail generator software code to filter the plurality of frames to obtain a plurality of key frame candidates, determine a ranking of the plurality of key frame candidates based in part on a blur detection analysis and an image distribution analysis of each of the plurality of key frame candidates, and generate a thumbnail based on the ranking

RELATED APPLICATIONS

This application is related to application Ser. No. 14/793,584, filedJul. 7, 2015, titled “Systems and Methods for Automatic Key FrameExtraction and Storyboard Interface Generation for Video,” and commonlyassigned with the present application. That related application ishereby incorporated fully by reference into the present application.

BACKGROUND

Due to its nearly universal popularity as a content medium, ever morevideo is being produced and made available to users. As a result, theefficiency with which video content can be reviewed, edited, and managedhas become increasingly important to producers of video content andconsumers of such content alike. For example, improved techniques forreviewing video content, such as the use of key frames or thumbnailsrepresentative of a given shot within a video file, may reduce the timespent in video production and management, as well as the time requiredfor a user to navigate within the video content.

In order for a key frame or thumbnail to effectively convey the subjectmatter of the shot it represents, the images appearing in the thumbnail,as well as the composition of those images, should be both appealing andintuitively recognizable. In addition to content and composition,however, the effectiveness of a key frame or thumbnail in conveying thesubject matter of a shot may further depend on the features of thedisplay device used to view the representative image.

SUMMARY

There are provided video processing systems and methods for performingthumbnail generation, substantially as shown in and/or described inconnection with at least one of the figures, and as set forth morecompletely in the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a diagram of an exemplary video processing system forperforming thumbnail generation, according to one implementation;

FIG. 2 shows an exemplary system and a computer-readable non-transitorymedium including instructions for performing thumbnail generation,according to one implementation;

FIG. 3 shows a flowchart presenting an exemplary method for performingthumbnail generation, according to one implementation;

FIG. 4 shows an exemplary key frame identification module of a thumbnailgenerator software code suitable for execution by a hardware processorof the systems shown by FIGS. 1 and 2, according to one implementation;

FIG. 5 shows an exemplary frame analysis module of a thumbnail generatorsoftware code suitable for execution by a hardware processor of thesystems shown by FIGS. 1 and 2, according to one implementation; and

FIG. 6 shows exemplary predetermined distribution patterns suitable foruse in ranking candidate key frames for thumbnail generation, accordingto one implementation.

DETAILED DESCRIPTION

The following description contains specific information pertaining toimplementations in the present disclosure. One skilled in the art willrecognize that the present disclosure may be implemented in a mannerdifferent from that specifically discussed herein. The drawings in thepresent application and their accompanying detailed description aredirected to merely exemplary implementations. Unless noted otherwise,like or corresponding elements among the figures may be indicated bylike or corresponding reference numerals. Moreover, the drawings andillustrations in the present application are generally not to scale, andare not intended to correspond to actual relative dimensions.

As stated above, the efficiency with which video content can bereviewed, edited, and managed has become increasingly important toproducers of video content and consumers of such content alike. Forexample, improved techniques for reviewing video content, such as theuse of key frames or thumbnails representative of a given shot within avideo file, may reduce the time spent in video production andmanagement, as well as the time required for a user to navigate withinthe video content.

As further stated above, in order for a key frame or thumbnail toeffectively convey the subject matter of the shot it represents, theimages appearing in the thumbnail, as well as the composition of thoseimages, should be both appealing and intuitively recognizable. Inaddition to content and composition, however, the effectiveness of a keyframe or thumbnail in conveying the subject matter of a shot may furtherdepend on the features of the display device used to view therepresentative image.

The present application discloses a thumbnail generation solution thatsubstantially optimizes the selection and generation of one or morethumbnails corresponding respectively to one or more key frames of ashot within a video file. It is noted that, as used in the presentapplication, the term “shot” refers to a sequence of frames within thevideo file that are captured from a unique camera perspective withoutcuts and/or other cinematic transitions.

As is further described below, by ranking key frame candidates for aparticular shot based in part on a blur detection analysis of each keyframe candidate at multiple levels of granularity, the presentapplication discloses a thumbnail generation solution thatadvantageously provides thumbnails including clear, recognizable images.By further ranking key frame candidates based in part on an imagedistribution analysis of each key frame candidate, the presentapplication discloses a thumbnail generation solution thatadvantageously provides thumbnails including intuitively identifiablesubject matter. Moreover, by yet further ranking key frame candidatesbased in part on display attributes of a user device, the presentapplication discloses a thumbnail generation solution thatadvantageously provides thumbnails that are substantially optimized forviewing by a user.

FIG. 1 shows a diagram of one exemplary implementation of a videoprocessing system for performing thumbnail generation. As shown in FIG.1, video processing system 100 includes computing platform 102 havinghardware processor 104, and system memory 106 implemented as anon-transitory storage device. According to the present exemplaryimplementation, system memory 106 stores thumbnail generator softwarecode 110 including key frame identification module 120, frame analysismodule 130, and thumbnail generation module 140.

As further shown in FIG. 1, video processing system 100 is implementedwithin a use environment including communication network 108, userdevice 150 including display 152, and user 154 utilizing user device150. Also shown in FIG. 1 are network communication links 118interactively connecting user device 150 and video processing system 100via communication network 108, video file 116, and one or morethumbnail(s) 112 generated using thumbnail generator software code 110.

It is noted that although FIG. 1 depicts thumbnail generator softwarecode 110 including key frame identification module 120, frame analysismodule 130, and thumbnail generation module 140 as being stored in itsentirety in memory 106, that representation is merely provided as an aidto conceptual clarity. More generally, video processing system 100 mayinclude one or more computing platforms 102, such as computer serversfor example, which may be co-located, or may form an interactivelylinked but distributed system, such as a cloud based system, forinstance.

As a result, hardware processor 104 and system memory 106 may correspondto distributed processor and memory resources within video processingsystem 100. Thus, it is to be understood that various portions ofthumbnail generator software code 110, such as one or more of key frameidentification module 120, frame analysis module 130, and thumbnailgeneration module 140, may be stored and/or executed using thedistributed memory and/or processor resources of video processing system100.

According to the implementation shown by FIG. 1, user 154 may utilizeuser device 150 to interact with video processing system 100 overcommunication network 108. In one such implementation, video processingsystem 100 may correspond to one or more web servers, accessible over apacket network such as the Internet, for example. Alternatively, videoprocessing system 100 may correspond to one or more computer serverssupporting a local area network (LAN), or included in another type oflimited distribution network.

Although user device 150 is shown as a personal computer (PC) in FIG. 1,that representation is also provided merely as an example. In otherimplementations, user device 150 may be any other suitable mobile orstationary computing device or system. For example, in otherimplementations, user device 150 may take the form of a smart TV, laptopcomputer, tablet computer, digital media player, gaming console, orsmartphone, for example. User 154 may utilize user device 150 tointeract with video processing system 100 to use thumbnail generatorsoftware code 110, executed by hardware processor 104, to generatethumbnail(s) 112.

It is noted that, in various implementations, thumbnail(s) 112, whengenerated using thumbnail generator software code 110, may be stored insystem memory 106 and/or may be copied to non-volatile storage (notshown in FIG. 1). Alternatively, or in addition, as shown in FIG. 1, insome implementations, thumbnail(s) 112 may be sent to user device 150including display 152, for example by being transferred via networkcommunication links 118 of communication network 108. It is furthernoted that display 152 may take the form of a liquid crystal display(LCD), a light-emitting diode (LED) display, an organic light-emittingdiode (OLED) display, or another suitable display screen that performs aphysical transformation of signals to light.

FIG. 2 shows exemplary system 260 and computer-readable non-transitorymedium 214 including instructions for performing thumbnail generation,according to one implementation. System 260 includes computer 268 havinghardware processor 264 and system memory 266, interactively linked todisplay 262. Display 262 may take the form of an LCD, LED display, anOLED display, or another suitable display screen that performs aphysical transformation of signals to light. System 260 includingcomputer 268 having hardware processor 264 and system memory 266corresponds in general to video processing system 100 includingcomputing platform 102 having hardware processor 104 and system memory106, in FIG. 1. Consequently, system 260 may share any of thecharacteristics attributed to corresponding video processing system 100by the present disclosure.

Also shown in FIG. 2 is computer-readable non-transitory medium 214having thumbnail generator software code 210 stored thereon. Theexpression “computer-readable non-transitory medium,” as used in thepresent application, refers to any medium, excluding a carrier wave orother transitory signal, that provides instructions to hardwareprocessor 264 of computer 268. Thus, a computer-readable non-transitorymedium may correspond to various types of media, such as volatile mediaand non-volatile media, for example. Volatile media may include dynamicmemory, such as dynamic random access memory (dynamic RAM), whilenon-volatile memory may include optical, magnetic, or electrostaticstorage devices. Common forms of computer-readable non-transitory mediainclude, for example, optical discs, RAM, programmable read-only memory(PROM), erasable PROM (EPROM), and FLASH memory.

According to the implementation shown in FIG. 2, computer-readablenon-transitory medium 214 provides thumbnail generator software code 210for execution by hardware processor 264 of computer 268. Thumbnailgenerator software code 210 corresponds in general to thumbnailgenerator software code 110, in FIG. 1, and is capable of performing allof the operations attributed to that corresponding feature by thepresent disclosure. In other words, thumbnail generator software code210 includes a key frame identification module (not shown in FIG. 2), aframe analysis module (also not shown in FIG. 2), and a thumbnailgeneration module (also not shown in FIG. 3), corresponding respectivelyin general to key frame identification module 120, frame analysis module130, and thumbnail generation module 140, in FIG. 1.

The functionality of thumbnail generator software code 110/210 will befurther described by reference to FIG. 3 in combination with FIGS. 1, 2,4, 5, and 6. FIG. 3 shows flowchart 300 presenting an exemplary methodfor use by a system, such as video processing system 100, in FIG. 1, orsystem 260, in FIG. 2, to perform thumbnail generation. FIG. 4 showsexemplary key frame identification module 420 suitable for execution byhardware processor 104/264 as part of thumbnail generator software code110/210, according to one implementation. FIG. 5 shows exemplary frameanalysis module 530, also suitable for execution by hardware processor104/264 as part of thumbnail generator software code 110/210, accordingto one implementation. FIG. 6 shows exemplary predetermined distributionpatterns 682, 684, 686 a, and 686 b suitable for use in rankingcandidate key frames for generation of thumbnail(s) 112, according toone implementation.

Referring now to FIG. 3 in combination with FIGS. 1 and 2, flowchart 300begins with receiving video file 116 (action 302). By way of example,user 154 may utilize user device 150 to interact with video processingsystem 100 or system 260 in order to generate thumbnail(s) 112representative of a shot in video file 116. As shown by FIG. 1, user 154may do so by transmitting video file 116 from user device 150 to videoprocessing system 100 via communication network 108 and networkconununication links 118. Alternatively, video file 116 may be receivedfrom a third party source of video content, or may reside as a storedvideo asset of system memory 106/266. Video file 116 may be received bythumbnail generator software code 110/210, executed by hardwareprocessor 104/264.

Flowchart 300 continues with identifying shots in video file 116, eachshot including multiple frames of video file 116 (action 304). As notedabove, a “shot” refers to a sequence of frames within a video file thatare captured from a unique camera perspective without cuts and/or othercinematic transitions. Thus, video file 116 includes multiple shots,with each shot including multiple frames, such as two or more shots,each including two or more frames, for example. Identification of shotsin video file 116 may be performed by thumbnail generator software code110/210, executed by hardware processor 104/264.

Referring to FIG. 4, FIG. 4 shows an exemplary implementation of keyframe identification module 420 including shot detector 421, flashframes filter 423, dissolve frames filter 425, darkness filter 427, andblur filter 429. In addition, FIG. 4 shows video file 416, multipleshots 470 in video file 416, including shot 472, and multiple frames 474a, 474 b, and 474 c of shot 472 following filtering of shot 472 usingrespective flash frames filter 423, dissolve frames filter 425, anddarkness filter 427. Also shown in FIG. 4 are multiple key framecandidates 476 provided by key frame identification module 420 as anoutput.

Video file 416 and key frame identification module 420 correspondrespectively in general to video file 116 and key frame identificationmodule 120 of thumbnail generator software code 110/210. Consequently,key frame identification modules 120 and 420 may share any of thecharacteristics attributed to either of those corresponding features bythe present disclosure.

Identification of shots 470 in video file 116/416 may be performed bythumbnail generator software code 110/210, executed by hardwareprocessor 104/264, and using shot detector 421 of key frameidentification module 120/420. In some implementations, identifyingdiscrete shots 470 of video file 116/416 may be based on detecting shotboundaries. For example, shot boundaries of shot 472 may include one ormore of a starting frame of shot 472 and an ending frame of shot 472.

In some implementations, shot detector 421 may be configured todetermine starting and/or ending frames based on triggers and/or otherinformation contained in video file 116/416. Examples of triggers mayinclude one or more of a fade-in transition, a fade-out transition, anabrupt cut, a dissolve transition, and/or other triggers associated witha shot boundary. In some implementations, recognizing triggers may beaccomplished by shot detector 421 using image processing techniques suchas comparing one or more frames within a given time window to determinean occurrence of significant changes. For example, determining that asignificant change has occurred may be accomplished using a histogram ofcolor for individual frames, direct comparison of frames, or bydetecting chaotic optical flow.

In some implementations, shot detector 421 may be configured to identifyshots 470 based on rank-tracing techniques. For example, rank-tracingmay be accomplished by determining a histogram of frames of a videobased on a hue-saturation-value (HSV) color space model of individualframes, a hue-saturation-lightness (HSL) color space model of individualframes, and/or based on other techniques for representing an RGB colormodel of a frame.

Flowchart 300 continues with, for each of shots 470, e.g., shot 472,filtering frames 474 a, 474 b, and 474 c to obtain multiple key framecandidates 476 for shot 472 (action 306). Filtering of each of shots470, such as shot 472, to obtain key frame candidates 476 for shot 472,may be performed by thumbnail generator software code 110/210, executedby hardware processor 104/264, and using flash frames filter 423,dissolve frames filter 425, darkness filter 427, and blur filter 429 ofkey frame identification module 120/420.

Referring to shot 472 of shots 470 for exemplary purposes, flash framesincluded among the frames of shot 472 can be immediately eliminated aspotential key frame candidates 476 for shot 472 because they are notrelated to the content of video file 116/416. As a result, flash framesmay be filtered out of the frames of shot 472 using flash frames filter423, resulting in filtered frames 474 a of shot 472.

Dissolve frames are the result of superimposing two different images,and are therefore unsuitable as key frame candidates 476 for generationof thumbnail(s) 112 for shot 472. Consequently, dissolve frames may befiltered out of frames 474 a using dissolve frames filter 425. Dissolveframes may be filtered out of frames 474 a using dissolve frames filter425 by identifying portions of video file 116/416 in which the color ofeach pixel evolves in a nearly linear fashion along contiguous frames.Elimination of dissolve frames from frames 474 a results in furtherfiltered frames 474 b of shot 472.

Frames that are dark are typically also not suitable as key framecandidates 476 for generation of thumbnail(s) 112 for shot 472. As aresult, dark frames may be filtered out of frames 474 b using darknessfilter 427. Dark frames may be filtered out of frames 474 b usingdarkness filter 427 through analysis of the light histogram for each offrames 474 b. Those frames among frames 474 b failing to meet apredetermined lightness threshold may be filtered out of frames 474 b,resulting in yet further filtered frames 474 c of shot 472.

Frames that are blurry are typically also not suitable as key framecandidates 476 for generation of thumbnail(s) 112 for shot 472 becausethey fail to clearly convey the subject matter of shot 472. As a result,blurry frames may be filtered out of frames 474 c using blur filter 429.

Blurry frames may be filtered out of frames 474 c using blur filter 429by sampling at least some corners in the frame previous to the frame ofinterest, tracking the positions of those corners on the subsequentframe, i.e., the frame of interest, and determining a measure of blurbased on the mean and variance of the magnitudes of the movement vectorsof the corners. If the measure of blur is above a predeterminedthreshold, there are either too many points that move too fast, i.e.,substantially the entire frame is blurry, or relatively few points moveeven faster, i.e., a local area of the frame is blurry. In either case,the blurry frame is filtered out and eliminated as a key framecandidate. It is noted that if the number of corners included in theframe is too small to perform the analysis, the frame may also beeliminated.

According to the exemplary implementation shown in FIG. 4, filtering ofthe is frames of shot 472 by thumbnail generator software code 110/210,executed by hardware processor 104/264, and using flash frames filter423, dissolve frames filter 425, darkness filter 427, and blur filter429 of key frame identification module 120/420, obtains multiple keyframe candidates 476 for use in generating one or more thumbnails 112for shot 472. Thus, the filtering of the frames of shot 472 by thumbnailgenerator software code 110/210, executed by hardware processor 104/264,and using key frame identification module 120/420, substantiallyeliminates transition frames, dark frames, and blurred frames inobtaining key frame candidates 476 for shot 472. Similarly, others ofshots 470 can be filtered using flash frames filter 423, dissolve framesfilter 425, darkness filter 427, and blur filter 429 of key frameidentification module 120/420 to substantially eliminate transitionframes, dark frames, and blurred frames in obtaining key framecandidates for those respective shots.

Flowchart 300 continues with, for each of shots 470, e.g., shot 472,determining a ranking of key frame candidates 476 based in part on ablur detection analysis and an image distribution analysis of each keyframe candidate (action 308). Referring to FIG. 5, FIG. 5 shows anexemplary implementation of frame analysis module 530 including featureanalyzer 532, key feature detector 534, text analyzer 536, blur analyzer538, and image distribution analyzer 580. In addition, FIG. 5 shows keyframe candidates 576, and key frame ranking 578 determined followinganalysis of key frame candidates 576 using feature analyzer 532, keyfeature detector 534, text analyzer 536, blur analyzer 538, and imagedistribution analyzer 580.

Key frame candidates 576 and frame analysis module 530 correspondrespectively in general to key frame candidates 476 and frame analysismodule 130 of thumbnail generator software code 110/210. Consequently,frame analysis modules 130 and 530 may share any of the characteristicsattributed to either of those corresponding features by the presentdisclosure. Determining key frame ranking 578 of key frame candidates476/576 based in part on a blur detection analysis and an imagedistribution analysis of each of key frame candidates 476/576 may beperformed by thumbnail generator software code 110/210, executed byhardware processor 104/264, and using frame analysis module 130/530.

Feature analyzer 532 of frame analysis module 130/530 may be configuredto determine features of individual key frame candidates 476/576. Forexample, features of an individual key frame candidate may include oneor more of a relative size, position, and/or angle of one or moreindividual faces depicted in the key frame candidate, a state of a mouthand/or eyes of a given face, an image quality, one or more actions thatmay be taking place, and one or more background features appearing inthe key frame candidate. Actions taking place in key frame candidates476/576 may include one or more of explosions, car chases, and/or otheraction sequences. Feature analyzer 532 may detect features in key framecandidates 476/576 using one or more of “speeded up robust features”(SURF), “scale-invariant feature transform” (SIFT), and/or othertechniques.

In some implementations, feature analyzer 532 may be configured todetect one or more faces in individual key frame candidates and/or trackindividual faces is over one or more frames. Face detection and/ortracking may be accomplished using object recognition, patternrecognition, searching for a specific pattern expected to be present infaces, and/or other image processing techniques. By way of example, facedetection and/or tracking may be accomplished using a “sophisticatedhigh-speed object recognition engine” (SHORE), Viola-Jones objectdetection framework, and/or other techniques.

Key feature detector 534 of frame analysis module 130/530 may beconfigured to determine which of the one or more features identified byfeature analyzer 532 may be classified as important in a given key framecandidate. In some implementations, importance may correspond to acharacter's role in the video, and/or other measures of importance. Arole may include one of a speaker, a listener, a primary actor, asecondary actor, a background actor, a temporary or transient actor, oran audience member or spectator, for example.

In some implementations, key feature detector 534 may be configured todetermine the importance of a face based on various features of thegiven face, for example. In some implementations, one or more featuresof a face may include the determined relative position, size, and/orangle of a given face respect to the camera capturing the key framecandidate, the state of the mouth and the eyes, and/or whether the faceis detected over multiple frames, for example.

As a specific example, key frame candidates 476/576 may include one ormore characters speaking, one or more characters listening, and one ormore persons acting as spectators to the speaking and listening. A givenspeaker and/or a given listener may be depicted in a key frame candidateas being positioned closer to the camera relative to the one or morespectators positioned in the background of the key frame candidate.Consequently, the speaker and/or listener may have face sizes that maybe relatively larger than the face sizes of the one or more spectators.Key feature detector 534 may be configured to determine that thedetected faces of the speaker and/or listener are a key feature orfeatures having greater importance than the detected faces of the one ormore spectators.

Text analyzer 536 of frame analysis module 130/530 may be configured todetect text displayed in key frame candidates 476/576. In someimplementations, for example, text (e.g., a sentence and/or other textstring) may be detected using text detection techniques such as StrokeWidth Transform (SWT), high frequency analysis of the image includingrefinement stages based on machine learning, and/or other techniques.

Blur analyzer 538 of frame analysis module 130/530 may be configured todetect blurriness in key frame candidates 476/576 that is either toosubtle or too localized to have been detected and filtered out usingblur filter 429 of key frame identification module 120/420. Blurrinesswithin key frame candidates 476/576 may be identified using bluranalyzer 538 by detecting many or substantially all corners in the frameprevious to the key frame candidate of interest, tracking the positionsof those corners on the subsequent frame, i.e., the key frame candidateof interest, and determining a measure of blur based on the mean andvariance of the magnitudes of the movement vectors of the corners.

Key frame ranking 578 of key frame candidates 476/576 is based at leastin part on the blur detection analysis performed by blur analyzer 538 offrame analysis module 530. That is to say, the higher the measure ofblur associated with a key frame candidate, the lower key frame ranking578 of that particular key frame candidate would typically be, i.e., itwould be ranked as relatively less desirable for use in generatingthumbnail(s) 112.

Image distribution analyzer 580 of frame analysis module 130/530 may beconfigured to determine the desirability with which images, such asfeatures, key features, and text, are distributed within each of keyframe candidates 476/576. In some implementations, for example, theimage distribution analysis performed by image distribution analyzer 580of frame analysis module 130/530 may include evaluating the distributionof images in each of key frame candidates 476/576 relative to one ormore predetermined distribution patterns.

Referring to FIG. 6, FIG. 6 shows exemplary predetermined distributionpatterns 682, 684, and 686 a and 686 b suitable for use in rankingcandidate key frames for thumbnail generation, according to oneimplementation. Predetermined distribution pattern 682 is a rule ofthirds distribution pattern corresponding to the rule of thirdsguideline for composing visual images. Predetermined distributionpattern 684 is a golden mean distribution pattern corresponding to thegolden mean or golden ratio approach to proportioning an object orimage. Predetermined distribution patterns 686 a and 686 b are goldentriangle distribution patterns corresponding to the classical goldentriangle rule of composition used in painting and photography.

Thus, the image distribution analysis of key frame candidates 476/576performed by image distribution analyzer 580 of frame analysis module130/530 may include evaluating the distribution of images in each of keyframe candidates 476/576 relative to one or more of rule of thirdsdistribution pattern 682, golden mean distribution pattern 684, andgolden triangle distribution patterns 686 a and 686 b. Key frame ranking578 of key frame candidates 476/576 is based at least in part on theimage distribution analysis performed by image distribution analyzer 580of frame analysis module 530. That is to say, the more closely thedistribution of images in a particular key frame candidate comports withone or more predetermined image distribution patterns, the higher keyframe ranking 578 of that particular key frame candidate would typicallybe, i.e., it would be ranked as relatively more desirable for use ingenerating thumbnail(s) 112.

Flowchart 300 can conclude with, for each of shots 470, e.g., shot 472,generating at least one thumbnail 112(s) for shot 472 based on key frameranking 578 (action 310). Generation of at least one thumbnail(s) 112for shot 472 can be performed by thumbnail generator software code110/210, executed by hardware processor 104/264, and using thumbnailgeneration module 140.

In some implementations, generation of at least one thumbnail(s) 112 caninclude improving the image quality of the key frame candidate(s) fromwhich thumbnail(s) 112 is/are generated by cropping the key framecandidate(s) and/or by performing contrast enhancement of the key framecandidate(s). For example, the contrast of the thumbnail candidate(s)can be enhanced by equalizing its/their lightness histogram(s). However,it may be advantageous or desirable for only the region between twogiven percentiles to be equalized and adjusted to a new given range,whereas the lower and upper tails are linearly transformed to maintainthe continuity of the pixels in the image. In other words, given p_(L)and p_(U), the lower and upper percentiles respectively, and v_(L) andv_(U), the new values to which such percentiles are to be mapped, thetransformation function is given by Equation 1 as follows:

${{eq}(x)} = \left\{ \begin{matrix}{{\frac{x}{{ppf}\left( p_{L} \right)} \cdot v_{L}},{0 \leq x \leq {{ppf}\left( p_{L} \right)}}} \\{{v_{L} + {\frac{\sum\limits_{y = {{{ppf}{(p_{L})}} + 1}}^{x}{{hist}(y)}}{\sum\limits_{z = {{{ppf}{(p_{L})}} + 1}}^{{ppf}{(p_{U})}}{{hist}(z)}} \cdot \left( {v_{U} - v_{L}} \right)}},{{{ppf}\left( p_{L} \right)} < x \leq {{ppf}\left( p_{U} \right)}}} \\{{v_{U} + {\frac{x - {{ppf}\left( p_{U} \right)}}{v_{U} - {{ppf}\left( p_{U} \right)}} \cdot \left( {100 - v_{U}} \right)}},{{{ppf}\left( p_{U} \right)} < x \leq 100}}\end{matrix} \right.$

where ppf is the percent point function, also known as the quantilefunction, and hist is the histogram of the original image.

In some implementations, the exemplary method outlined in flowchart 300may further include sending thumbnail(s) 112 to user device 150including display 152. Sending of thumbnail(s) 112 to user device 150,may be performed by thumbnail generator software code 110/210, executedby hardware processor 104/264, for example by being transferred vianetwork communication links 118 of communication network 108. In thoseimplementations, key frame candidate ranking 578 of key frame candidates476/576 for shot 472 may be determined based further in part on displayattributes of the user device 150.

For example, when user device 150 takes the form of a mobile device,such as a smartphone, digital media player, or small form factor tabletcomputer, key frame candidates including relatively large features andrelatively simple structures may be ranked more highly for generation ofthumbnail(s) 112. By contrast, when user device 150 takes the form of asmart TV or PC, for example, key frame candidates including relativelysmaller features and more complex layouts may be ranked more highly forgeneration of thumbnail(s) 112. Moreover, in any of those use cases,generation of thumbnail(s) 112 may include cropping the key framecandidate(s) used to produce thumbnail(s) 112 in order to fit the aspectratio of display 152 of user device 150.

Thus, the present application discloses a thumbnail generation solutionthat substantially optimizes the selection and generation of one or morethumbnails corresponding respectively to one or more key frames of ashot within a video file. By ranking key frame candidates for aparticular shot based in part on a blur detection analysis of each keyframe candidate at multiple levels of granularity, the presentapplication discloses a thumbnail generation solution thatadvantageously provides thumbnails including clear, recognizable images.In addition, by further ranking key frame candidates based in part on animage distribution analysis of each key frame candidate, the presentapplication discloses a thumbnail generation solution thatadvantageously provides thumbnails including intuitively identifiablesubject matter. Moreover, by yet further ranking key frame candidatesbased in part on display attributes of a user device, the presentapplication discloses a thumbnail generation solution thatadvantageously provides thumbnails that are substantially optimized forinspection by a user.

From the above description it is manifest that various techniques can beused for implementing the concepts described in the present applicationwithout departing from the scope of those concepts. Moreover, while theconcepts have been described with specific reference to certainimplementations, a person of ordinary skill in the art would recognizethat changes can be made in form and detail without departing from thescope of those concepts. As such, the described implementations are tobe considered in all respects as illustrative and not restrictive. Itshould also be understood that the present application is not limited tothe particular implementations described herein, but manyrearrangements, modifications, and substitutions are possible withoutdeparting from the scope of the present disclosure.

1. A video processing system comprising: a computing platform includinga hardware processor and a system memory; a thumbnail generator softwarecode stored in the system memory; the hardware processor configured toexecute the thumbnail generator software code to: receive a video file;identify a plurality of shots in the video file, each of the pluralityof shots including a plurality of frames of the video file; and for eachof the plurality of shots: filter the plurality of frames to obtain aplurality of key frame candidates; determine a ranking of the pluralityof key frame candidates based in part on a blur detection analysis andan image distribution analysis of each of the plurality of key framecandidates, wherein the image distribution analysis determines howclosely a pattern of distribution of images within each of the pluralityof key frame candidates comports with a predetermined distributionpattern, and wherein the closer the pattern of distribution of imageswithin a key frame candidate is to the predetermined distributionpattern, the higher is the ranking of the key frame candidate; andgenerate a thumbnail based on the ranking.
 2. The video processingsystem of claim 1, wherein the hardware processor is further configuredto execute the thumbnail generator software code to send the thumbnailto a user device including a display.
 3. The video processing system ofclaim 2, wherein the ranking of the plurality of key frame candidates isdetermined based further in part on display attributes of the userdevice.
 4. (canceled)
 5. The video processing system of claim 1, whereinthe predetermined distribution pattern includes one or more of a rule ofthirds distribution pattern, a golden mean distribution pattern, and agolden triangle distribution pattern.
 6. The video processing system ofclaim 1, wherein the hardware processor is configured to execute thethumbnail generator software code to generate the thumbnail byperforming at least one of a cropping and a contrast enhancement of arespective at least one of the plurality of key frame candidatescorresponding to the thumbnail.
 7. The video processing system of claim1, wherein the hardware processor is configured to execute the thumbnailgenerator software code to filter the plurality of frames tosubstantially eliminate transition frames, dark frames, and blurredframes in order to obtain the plurality of key frame candidates.
 8. Amethod for use by a video processing system including a computingplatform having a hardware processor and a system memory storing athumbnail generator software code for execution by the hardwareprocessor, the method comprising: receiving, using the hardwareprocessor, a video file; identifying, using the hardware processor, aplurality of shots in the video file, each of the plurality of shotsincluding a plurality of frames of the video file; and for each of theplurality of shots: filtering, using the hardware processor, theplurality of frames to obtain a plurality of key frame candidates;determining, using the hardware processor, a ranking of the plurality ofkey frame candidates based in part on a blur detection analysis and animage distribution analysis of each of the plurality of key framecandidates, wherein the image distribution analysis determines howclosely a pattern of distribution of images within each of the pluralityof key frame candidates comports with a predetermined distributionpattern, and wherein the closer the pattern of distribution of imageswithin a key frame candidate is to the predetermined distributionpattern, the higher is the ranking of the key frame candidate; andgenerating, using the hardware processor, a thumbnail based on theranking.
 9. The method of claim 8, further comprising sending, using thehardware processor, the thumbnail to a user device including a display.10. The method of claim 9, wherein the ranking of the plurality of keyframe candidates is determined based further in part on displayattributes of the user device.
 11. (canceled)
 12. The method of claim 8,wherein the predetermined distribution pattern includes one or more of arule of thirds distribution pattern, a golden mean distribution pattern,and a golden triangle distribution pattern.
 13. The method of claim 8,wherein generating the thumbnail includes performing at least one of acropping and a contrast enhancement of a respective one of the pluralityof key frame candidates corresponding to the thumbnail.
 14. The methodof claim 8, wherein the hardware processor filters the plurality offrames to substantially eliminate transition frames, dark frames, andblurred frames in order to obtain the plurality of key frame candidates.15-20. (canceled)
 21. A video processing system comprising: a computingplatform including a hardware processor and a system memory; a thumbnailgenerator software code stored in the system memory; the hardwareprocessor configured to execute the thumbnail generator software codeto: obtain display attributes of a user device; receive a video file;identify a plurality of shots in the video file, each of the pluralityof shots including a plurality of frames of the video file; and for eachof the plurality of shots: filter the plurality of frames to obtain aplurality of key frame candidates; determine a ranking of the pluralityof key frame candidates based in part on a blur detection analysis,display attributes of the user device, and an image distributionanalysis of each of the plurality of key frame candidates, wherein theimage distribution analysis determines how closely a pattern ofdistribution of images within each of the plurality of key framecandidates comports with a predetermined distribution pattern, andwherein the closer the pattern of distribution of images within a keyframe candidate is to the predetermined distribution pattern, the higheris the ranking of the key frame candidate; and generate a thumbnailbased on the ranking.
 22. The video processing system of claim 21,wherein the hardware processor is further configured to execute thethumbnail generator software code to send the thumbnail to a display ofthe user device.
 23. The video processing system of claim 21, whereinthe predetermined distribution pattern includes one or more of a rule ofthirds distribution pattern, a golden mean distribution pattern, and agolden triangle distribution pattern.
 24. The video processing system ofclaim 21, wherein the hardware processor is configured to execute thethumbnail generator software code to generate the thumbnail byperforming at least one of a cropping and a contrast enhancement of arespective at least one of the plurality of key frame candidatescorresponding to the thumbnail.
 25. The video processing system of claim21, wherein the hardware processor is configured to execute thethumbnail generator software code to filter the plurality of frames tosubstantially eliminate transition frames, dark frames, and blurredframes in order to obtain the plurality of key frame candidates.